The Modern Data Stack in 2025: What You Need to Know

Data is the backbone of modern business. Over the past decade, the tools and architectures used to handle data have evolved dramatically. In 2025, we reached a new era of scalability, automation, and AI augmentation, enabled by a powerful framework known as the modern data stack (MDS).

What started as a buzzy catchphrase in tech circles, has matured into a foundational concept that shapes how organizations collect, transform, govern data. The modern data stack of 2025 is far more than a collection of cloud-native tools. It enables real-time insights, smarter decision-making, and faster innovation.

In this guide, we’ll walk you through what the modern data stack looks like in 2025 and how it has evolved, along with its core components, some use cases, and best practices for integrating it into your workflows. Whether you’re rebuilding legacy infrastructure or looking to future-proof your analytics architecture, this blueprint is essential for your success.

What is the modern data stack?

The modern data stack is a collection of interoperable, cloud-based tools and services, designed to manage the complete data lifecycle—from ingestion and storage to transformation, analysis, and activation. Unlike traditional systems that required manual integration and were difficult to scale, the modern stack is composable, elastic, and developer-friendly.

At its core, the modern stack enables you to:

• Collect data from diverse sources, like apps, APIs, and databases.
• Store data in a scalable cloud data platform.
• Transform and model data for business use.
• Analyze data using powerful business intelligence (BI) and artificial intelligence (AI) tools.
• Use insights by embedding them into workflows or operational systems.

Modern data stacks are built to support real-time data needs, self-service analytics, advanced machine learning (ML), and compliance with data privacy regulations.

A brief history: How we got here

To better understand the modern data stack in 2025, we can look back at how data architectures have evolved.

In the early 2010s, most organizations relied on their on-premises data warehouses and monolithic BI tools. Data pipelines were often rigid, expensive to maintain, and designed for batch processing. At the time, “big data” was the buzzword. However, few companies had the cloud infrastructure to use it effectively.

The turning point came with the rise of cloud-native platforms like Amazon Redshift, Google BigQuery, and Snowflake. These tools made scalable data storage more widely accessible, enabling organizations of all sizes to store and query massive data sets without managing physical servers.

Around the same time, companies like Fivetran, dbt, and Looker emerged, offering modular solutions to ingest, transform, and visualize data. By the mid-2010s, the phrase “modern data stack” began to gain traction among startups and data-savvy tech teams.

By 2022, the stack had become a mainstream concept. With more people working remotely and businesses embracing digital transformation and AI, enterprises realized that legacy systems couldn’t keep up. The modern data stack became the new standard.

Now in 2025, we’ve seen massive maturity in tools, architecture, and practices. AI has become embedded into nearly every layer of the stack. And the need for speed, governance, and insight has never been higher.

Core components of the modern data stack in 2025

The modern data stack is modular by design. Each component handles a specific function, allowing for flexibility, customization, and innovation.

Here’s what a typical 2025 stack includes:

1. Data ingestion and ELT tools

Tools like Fivetran, Airbyte, Stitch, and Supermetrics extract data from source systems—CRMs, SaaS apps, databases—and load it into a central warehouse. In 2025, most teams favor ELT (Extract, Load, Transform) over traditional ETL for better scalability and performance. With ELT, raw data is preserved in the warehouse, allowing for dynamic modeling and retrospective changes.

Many ingestion tools now support real-time sync via change data capture (CDC), making it easier to replicate on-prem data to the cloud with minimal lag. In addition, low-code interfaces and pre-built connectors have made integration faster and more accessible to teams without deep engineering resources.

2. Cloud data warehouses and lakehouses

These platforms are the backbone of your data architecture. In 2025, most organizations use:

• Snowflake: Known for keeping storage and computing functions separate, it is now enhanced with native support for Python and AI workloads.
• Databricks: A go-to for data lakehouse architecture, combining structured and unstructured data with built-in ML capabilities.
• BigQuery: Google’s fully managed serverless warehouse is deeply integrated with the rest of its cloud platform (GCP).

Lakehouse architectures are gaining popularity for their ability to store raw and processed data in a single environment, supporting both analytics and AI with minimal friction. The unified approach helps reduce data duplication and complexity while supporting a wide range of file formats and access patterns.

3. Data transformation tools

The industry leader remains dbt (data build tool), with competitors like Dataform and Coalesce offering alternatives. These tools allow teams to write SQL-based transformations that are version-controlled, testable, and reusable. Developers benefit from Git integration, modular codebases, and templating, reducing duplication and improving consistency.

In 2025, transformation tools are tightly integrated with Git-based workflows, observability platforms, and even AI assistants that auto-generate modeling logic or suggest optimizations. Many tools include visual editors and testing frameworks, making it easier for analysts and engineers to collaborate on trusted data models.

4. Business intelligence and analytics tools

Self-service analytics is the ultimate goal for many businesses, and modern BI tools deliver on that promise. They also now offer embedded analytics, allowing data teams to deliver insights directly inside other tools like Salesforce, Slack, or internal apps. In 2025, many platforms offer AI-generated narratives and explainability features, allowing users to not only see the data but also understand the “why” behind the trends.

5. Reverse ETL and data activation

This is where analytics meets operations. Reverse ETL tools like Hightouch, Census, and Weld sync data from the warehouse back to business systems—powering personalization, campaign automation, and customer support. These tools make the warehouse more than a place for analysis; it becomes the engine for action.

Many of these tools support event-driven architectures and integrate directly with customer data platforms (CDPs), CRMs, and feature flag tools. Newer solutions embrace “composable CDP” models, allowing companies to activate their data without vendor lock-in.

6. Observability, quality, and governance

Data stacks are only as reliable as they are trustworthy. Modern stacks include:

• Data monitoring tools (Monte Carlo, Metaplane, Databand) for tracking data pipelines, detecting anomalies, and managing SLAs.
• Data catalogs and governance platforms (Alation, Collibra, Atlan) that provide lineage, metadata management, access control, and compliance automation.

Governance has shifted upstream to the beginning of development instead of bolted on after the fact. In 2025, these tools increasingly include AI-powered tagging, PII detection, and impact analysis, helping teams move faster without compromising on trust or compliance.

7. AI agents and automation layers

The newest addition to the stack is intelligent automation. In 2025, AI-powered agents assist with:

• Suggesting metrics and queries
• Detecting schema drift
• Auto-documenting pipelines
• Providing business users with conversational analytics

This “agentic layer” is still maturing, but it’s quickly becoming essential for productivity and data democratization. As LLMs improve, these agents are beginning to act not just as assistants but as autonomous collaborators. They can recommend optimizations, propose new KPIs, or detect root causes of data anomalies. The organizations embracing this layer are already seeing a steep reduction in manual overhead and time-to-insight.

Why companies are investing in the modern data stack

Whether you’re a Fortune 100 company or a digital-first startup, the modern data stack solves critical pain points across the organization.

• Speed and agility. The stack enables teams to go from data to decisions in hours, not weeks. With schema-flexible ingestion, cloud scalability, and AI-assisted modeling, businesses can adapt to changing needs without rewriting entire pipelines. This means faster experimentation, quicker iterations, and the ability to respond to market shifts in real time.
• Data democratization. Business users don’t want to wait in line for insights. With modern tools, anyone with basic data literacy can explore trends, test hypotheses, or trigger workflows without needing SQL expertise. This shift from centralized gatekeeping to decentralized access accelerates innovation across departments.
• Scalability. The composable nature of the stack allows it to scale with your business. Add more sources, users, or use cases without overhauling your infrastructure. Whether supporting 10 or 10,000 users, the stack adjusts to meet demand.
• Operational efficiency. Modern pipelines are automated, observable, and resilient. Fewer manual handoffs mean fewer errors and more time for value-added work. Teams spend less time maintaining brittle scripts and more time delivering strategic insights.
• AI-enablement. AI and ML thrive on clean, accessible, well-governed data. The modern stack provides the foundation for training models, detecting anomalies, and enabling intelligent agents that support predictive and prescriptive analytics. From fraud detection to personalized recommendations, today’s AI-driven use cases depend on the flexible, high-quality data infrastructure the modern stack delivers.

Common use cases

The modern data stack supports real-world business outcomes. Some of the most common applications include:

• Customer 360: Combine data from marketing, support, and product to build a unified view of customer behavior. This empowers teams to personalize engagement, reduce churn, and improve lifetime value.
• Personalization: Feed analytics insights into reverse ETL workflows to power targeted campaigns or app experiences. By syncing data into tools like HubSpot, Braze, or Zendesk, companies can deliver relevant messaging at exactly the right time.
• Product analytics: Track user flows, feature usage, and retention cohorts in near real time. This helps product teams iterate faster and build experiences users actually want.
• Revenue forecasting: Use AI-driven dashboards and scenario modeling to predict growth, churn, or cost. Finance and ops teams can respond to shifting trends with more precision.

Operational efficiency: Identify bottlenecks in supply chains, onboarding, or support by analyzing system data. Automate reporting, spot anomalies, and drive down response times across business units.

How to build (or rebuild) your modern data stack in 2025

Implementing the modern stack doesn’t mean ripping everything out. In fact, the best approach is incremental. Here’s how to get started:

1. Start with a business use case

Rather than starting with tools, start with a question: What decision do you want to support with better data? Choose one domain (e.g., marketing attribution, sales forecasting) and build a vertical stack that delivers end-to-end value.

2. Choose a scalable data warehouse

Pick a cloud data warehouse that fits your volume, complexity, and budget. Most teams choose Snowflake, BigQuery, or Databricks—but make sure it integrates with your existing tools and workflows.

3. Ingest key data sources

Use ELT tools to start pulling in data from core systems. Begin with low-hanging fruit like Salesforce, Google Analytics, or internal databases.

4. Transform for analytics

Use dbt or similar tools to clean, model, and document your data. Create reusable models that reflect business logic—such as revenue calculations, active users, or churn definitions.

5. Enable self-service and activation

Deploy a BI tool that enables non-technical users to explore data on their own. Then, use reverse ETL to push insights into the tools where decisions happen.

6. Monitor, govern, and improve

Set up observability tools to track freshness, volume anomalies, and schema changes. Use data catalogs to maintain trust and compliance.

Pitfalls to avoid

While the modern data stack offers powerful capabilities, it’s not without challenges:

• Tool sprawl. It’s easy to overbuild. Keep your stack lean and focused on real use cases. When every team brings in its own tools without alignment, integration complexity grows, costs balloon, and collaboration suffers. Regular audits can help rationalize tools and eliminate redundancy.
• Lack of ownership. Define who owns each layer of the stack, from ingestion to insight. Without clear roles, problems fall through the cracks and pipelines stagnate. Ownership models should extend beyond engineering. Domain teams should be accountable for data quality and business logic as part of a data product mindset.
• Poor documentation. Document data models, assumptions, and pipelines. It saves time and builds trust. When documentation is neglected, teams waste hours rediscovering logic or debugging errors that stem from unknown transformations. Treat documentation as part of the development process (not an afterthought).
• No training or enablement. Even the best stack won’t deliver results if your team doesn’t know how to use it. Invest in training and onboarding. This includes creating internal courses, hands-on demos, and role-specific playbooks. Self-service only works when users are confident and empowered.

What’s next for the modern data stack?

The stack continues to evolve, and in 2025, three trends stand out:

1. Agentic AI

Every layer of the stack is gaining AI-powered assistants. From pipeline generation to anomaly detection to executive summaries, intelligent agents are accelerating work across roles. These agents enhance creativity, suggest transformations, and help business users phrase better questions. In some tools, AI copilots now monitor pipeline health, write documentation, and even proactively flag anomalous data points before they impact decisions.

The result? A more agile, intelligent, and proactive data culture.

2. Composable architectures

The rise of APIs and data modeling languages (like dbt and ThoughtSpot’s TML) is making it easier to build stack components that snap together like LEGO blocks. This unlocks flexibility without sacrificing control. Organizations can mix and match best-in-class tools while maintaining a consistent metadata layer and governance model. Developers benefit from reusability, while analysts gain the freedom to innovate without breaking production systems. The composable approach is transforming the stack from a rigid pipeline into a dynamic ecosystem.

3. Data mesh and federated ownership

More companies are embracing decentralized models, where domain teams own their data products. Combined with modern stack tools, this leads to more scalable and sustainable architectures.

Instead of relying on a centralized data team for every request, product, marketing, and operations teams now own and manage their own trusted data assets, complete with documentation, quality checks, and access controls.

With proper training and tooling, data becomes a first-class citizen across the organization. The modern data stack makes this shift possible by reducing technical barriers and encouraging accountability.

Your path to business success: The modern business stack

The modern data stack has come a long way, from early startup adoption in 2015 to enterprise mainstay in 2025. Today, it powers everything from personalization engines to executive dashboards to AI copilots.

But like any good architecture, it’s not about the tools—it’s about outcomes. The best data stacks are those that support clear goals, grow with your business, and empower your people.

If you’re still running on legacy infrastructure, now is the time to explore what’s possible. And if you’ve already made the leap, take a step back and ask: Are we building the right things? Are our tools serving us or slowing us down?

The modern data stack isn’t just modern anymore. It’s mission-critical.

Explore how Domo helps you modernize your data stack and turn it into a real driver of business value.